Course overview

STAT150

Welcome to the homepage for STAT150: R for Data Science. This course is taught by Dr. Ihor Miroshnychenko in Autumn 2025 at the Kyiv School of Economics.

🚀 Why learn R?

Data is the new language of business, economics, and science. In this course, you won’t just learn “how to code” — you will learn how to discover stories hidden within data.

R is not just a programming language; it is a vast ecosystem designed specifically for data analysis. Whether you want to visualize economic trends, analyze marketing metrics, or automate reports, R is the industry standard tool used by companies like Google, Facebook, and Uber.

NoteIs this course for me?

Yes! This course is designed for beginners. We assume no prior programming experience. We start from zero and build up to professional-grade data analysis skills using the modern “Tidyverse” approach.

🎯 What you will learn

By the end of this course, you will possess the toolkit to turn raw data into actionable insights. We will focus on the practical cycle of Data Science:

  • Import: Getting data from files, databases, or APIs.
  • Tidy: Cleaning and organizing messy real-world data.
  • Transform: Selecting, filtering, and summarizing key information.
  • Visualize: Creating stunning, publication-quality graphics with ggplot2.
  • Communicate: Publishing your results using Quarto and Markdown.

🛠️ Course Philosophy

We believe in hands-on learning. You won’t just memorize syntax; you will solve real problems. The curriculum follows the philosophy of the famous book R for Data Science by Hadley Wickham, focusing on writing code that is easy to read, easy to share, and easy to reproduce.

“Data science is an exciting discipline that allows you to turn raw data into understanding, insight, and knowledge.” — Hadley Wickham

📅 Logistics

Ready to start your data journey? Navigate to the Syllabus tab to begin!

🎨 Course Identity

In the spirit of reproducible research, the visual identity for this course is created entirely using code! You can explore the collection of generated KSE course stickers in this GitHub repository.

Curious how the logo above was created? Click to reveal the source code.